I have a time series dataset of around 5k records.There are some outlier values counting 10.My aim is to predict these outlies…For that i tried opf and network models and also did different changes to the parameters like modifying resolution for encoding,alpha value but the outlier prediction is not happening…offcourse it is improving the prediction of commonly seen values.

So I wanted to know if outlier prediction is possible using nupic?If yes…how can i do that…should i change any specific parameter or any codebase which i can use.
Thanks in advance.

I am aiming for prediction not anomaly detection.
In the dataset ,the outliers are the values those are more than or equal to 90 . For these actual values, the prediction values are coming very less .Even for the actual values of more than or equal to 80, the predictions are coming less not impressive.

Do these higher outliers occur with any pattern? Like do they tend to come after certain values or sequences? The system can definitely predict those higher values, but only if they occur as part of some discernible pattern. The more complex the pattern the more repetitions it will take to learn. If they’re basically noise values on the other hand then it won’t predict them and they’ll produce high anomaly scores.

Also it may be worth trying a simple Scalar Encoder with a max-val equal to the minimum out-lier value. This will clip all higher inputs to 90 for example, so the encoder would group anything above 90 into the same encoding (basically a catch-all ‘out-lier’ category). This could help, though only to the extent that the outliers occur with some kind of temporal pattern.

@rhyolight…I have done the swarming. On the swarming result, i did different tuning on the parameter which improved prediction , but for higher values i am yet to see good result.I am trying to predict 4 step ahead.

Thank you for trying. It is hard for the system to predict outliers like without manual resets. For example, if you know the period of the pattern occurring, you could tell the TM to reset its current sequence state, essentially cutting off what it is learning. On the next time step it will start to learn a new sequence. These outliers are parts of a periodic pattern, and the only way to nail it down really is to reset at the same point in the period.

This is a crappy answer, I know, but we honestly don’t really know how temporal patterns are reset in the brain, I talked about this with Jeff in a video.